My first instinct was just a hashmap from message to last-printed timestamp, which is basically the right answer, but I spent too long second-guessing it.
Start by clarifying requirements and edge cases, then propose a hash map solution that stores the last printed timestamp for each message. Explain the O(1) time and space complexity, and discuss potential improvements like memory management for long-running systems.
Pro tip: Mention that in a real system, you'd need to handle memory growth by periodically cleaning up old entries or using an LRU cache, showing awareness of production concerns.
Ask about timestamp units (seconds vs milliseconds), whether timestamps are monotonically increasing, and if multiple messages can have the same timestamp. Confirm the 10-second window is inclusive or exclusive.
Propose using a hash map (dictionary) to store the last printed timestamp for each message. This allows O(1) lookup and update.
In shouldPrintMessage, check if the message exists in the map. If not, or if the current timestamp is at least 10 seconds greater than the stored timestamp, return true and update the map; otherwise return false.
State that time complexity is O(1) per operation and space complexity is O(n) where n is the number of unique messages. Discuss trade-offs.
Address memory growth in long-running systems. Suggest periodic cleanup of stale entries or using an LRU cache to bound memory usage.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.